Papers by Jingyao Tang
Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | Few-shot Event Detection (FSED) requires limited labeled data and expensive manual labeling. |
| Approach: | They propose a prototype-based prompt-instance Interaction with causal Intervention model to utilize both prompts and verbalizers and effectively eliminate all biases. |
| Outcome: | The proposed model utilizes both prompts and verbalizers and eliminates all biases on RAMS and ACE datasets. |
Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models with reasoning are single-granularity based on one element information, ignoring complementary fact of different granularities. |
| Approach: | They propose a document-level biomedical relation extraction model called FILR . it uses multi-dimensional information fusion and multi-granularity logic to obtain rich inferences . |
| Outcome: | The proposed model extracts all relation facts from biomedical documents . it is based on multi-dimensional information fusion and multi-granularity logic reasoning . the proposed model achieves state-of-the-art performance on two widely used biomedically corpora . |
Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for zero-shot event-relational reasoning require large computational resources and lack interpretability. |
| Approach: | They propose a method for Reasoning-Oriented Locating and Editing which locates and edits key modules of the language model for reasoning about event relations. |
| Outcome: | The proposed method improves interpretability and efficiency with reduced computational cost and achieves SOTA results in zero-shot event-relational reasoning. |
Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning. |
| Approach: | They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree. |
| Outcome: | The proposed model outperforms existing methods on TB-Dense and MATRES datasets. |
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)
Copied to clipboard
Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin
| Challenge: | Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model. |
| Approach: | They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model. |
| Outcome: | The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively. |